Deep Convective Transport to the Upper-Troposphere/Lower-Stratosphere

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)资助的。对流层上层和平流层下层(UT/LS)的气体对全球大气的化学和辐射预算有重大影响，这对理解气候变化至关重要。为了了解UT/LS区域的化学组成，需要更好地了解深对流输送。深对流中的输送的独特之处在于，它能够将近地表的成分夹带并迅速输送到UT/LS区域，在那里它们被去除训练。在当前的气候模式对流输送参数化中，风暴中卷吸和脱旋的可变性没有被捕捉到。首先，使用来自现场活动的双多普勒速度数据，将得出一组涵盖各种风暴形态和背景环境的风暴的观测偏离轮廓。其次，来自同一场战役的雷达反射率数据将被用来寻找速度导出的减速剖面和反射率场之间的关系。该提案第二部分的目标是确定一种处理雷达反射率观测的算法，以便能够根据天气雷达网覆盖的任何区域的雷达反射率观测来估计对流质量输送。第三，云分辨率模式将用于评估基于反射率的特定风暴传输估计的质量。利用雷达反射率观测和模式的结合，将编制美国中部对流输送的气候学。这项研究在理解和模拟深对流输送方面的改进将直接有益于许多补充的模拟工作，并提高对进入UT/LS的气体对流输送的理解。最终，这些结果可以用来减少气候预测模型中的不确定性，考虑到人类面临的气候挑战，这是一种迫切的需求。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The gases in the upper troposphere and lower stratosphere (UT/LS) have a significant effect on the chemical and radiative budgets of the global atmosphere, crucial to understanding climate change. To understand the chemical makeup of the UT/LS region, a much better understanding of deep convective transport is needed. Transport in deep convection is unique in its ability to entrain constituents from the near surface and rapidly transport them to the UT/LS region where they are detrained. The variability of entrainment and detrainment in storms is not captured in current climate model convective transport parameterizations.This study proposes to address these problems with a tripartite approach. First, using dual-Doppler velocity data from field campaigns, the observed detrainment profile will be derived for a suite of storms covering a range of storm morphologies and background environments. Second, radar reflectivity data from the same field campaigns will be used to look for relationships between velocity-derived detrainment profiles and the reflectivity fields. The goal of the second part of the proposal is to define an algorithm for processing radar reflectivity observations that will allow for estimates of convective mass transport based on radar reflectivity observations over any region covered by the weather radar network. Third, cloud-resolving models will be used to assess the quality of reflectivity-based transport estimates for specific storms. Using the combination of radar reflectivity observations and modeling, a climatology of convective transport over the central United States will be compiled.Improvements in understanding and modeling of deep convective transport resulting from this study will directly benefit many complementary modeling efforts and improve understanding of convective transport of gases into the UT/LS. Ultimately, these results can be used to reduce uncertainty in climate prediction models, a critical need considering the climate challenges facing humanity.